27 papers · ranked by Valyu relevance
Chensi Cao, Feng Liu, Hai Tan, Deshou Song + 5 more
'Weizhong Li' 'Yiming Zhou' 'Xiaochen Bo' 'Zhi Xie'] Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of…
Nikolaus Kriegeskorte
Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain and their computations could be…
Maria Trigka, Elias Dritsas, Chih-Chang Yu, Jian-Jiun Ding + 1 more
'Feng-Tsun Chien'] The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution…
Tim C Kietzmann, Patrick McClure, Nikolaus Kriegeskorte
The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to support cognitive function and behaviour. At the heart of the field are its models, i.e. mathematical and computational descriptions of the system being studied. These models typically map…
Vivienne Sze, Yu‐Hsin Chen, Tien-Ju Yang, Joel Emer
—Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable…
Darcie Anderson, Prabhakar Ramachandran, Jamie Trapp, Andrew Fielding
The use of machine learning has seen extraordinary growth since the development of deep learning techniques, notably the deep artificial neural network. Deep learning methodology excels in addressing complicated problems such as image classification, object detection, and natural language processing. A key feature of…
Jessica Loke, Noor Seijdel, Lukas Snoek, Matthew van der Meer + 4 more
Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks (DCNNs) but the computations underlying recurrent…
Patrick Thiam, Peter Bellmann, Hans A. Kestler, Friedhelm Schwenker
Standard feature engineering involves manually designing and assessing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features in order to optimize an inference model. Several studies have shown that this whole manual…
Malik YOUSEF, Jens ALLMER
Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic…
John Ball, Derek T. Anderson, Chee Seng Chan
In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications…
Jorge Valente, João António, Carlos Mora, Sandra Jardim + 1 more
The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges.…
Yu Li, Chao Huang, Lizhong Ding, Zhongxiao Li + 2 more
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities…
Mehdi Hassanpour, Marc Riera, Antonio González
—Data-intensive workloads and applications, such as machine learning (ML), are fundamentally limited by traditional computing systems based on the von-Neumann architecture. As data movement operations and energy consumption become key bottlenecks in the design of computing systems, the interest in unconventional…
Md. Shoaibur Rahman
This article presents an overview of the generalized formulations of the computations, optimization, and tuning of a deep feedforward neural network. A small network has been used to systematically explain the computing steps, which were then used to establish the generalized forms of the computations in forward and…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Yao-zhong Zhang, Seiya Imoto
Genomic sequences are traditionally represented as strings of characters: A (adenine), C (cytosine), G (guanine), and T (thymine). However, an alternative approach involves depicting sequence-related information through image representations, such as Chaos Game Representation (CGR) and read pileup images. With rapid…
Saptarshi Sengupta, Sanchita Basak, Pallabi Saikia, Sayak Paul + 4 more
'Vasilios Tsalavoutis' 'Frederick Ditliac Atiah' 'Vadlamani Ravi' 'Alan Peters'] Abstract—Deep learning has solved a problem that as little as five years ago was thought by many to be intractable - the automatic recognition of patterns in data; and it can do so with an accuracy that often surpasses that of human…
Yoonsuk Hyun, Doory Kim, Ana María Díez-Pascual
Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the…
Wout Bittremieux, Varun Ananth, William E. Fondrie, Carlo Melendez + 5 more
Protein tandem mass spectrometry data is most often interpreted by matching observed mass spectra to a protein database derived from the reference genome of the sample being analyzed. In many application domains, however, a relevant protein database is unavailable or incomplete, and in such settings de novo sequencing…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian, Melika Payvand + 3 more
'Melika Payvand' 'Elisa Donati' 'B. Linares-Barranco' 'Giacomo Indiveri'] Abstract—The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the…
Md. Zihad Bin Jahangir, Ruksat Hossain, Riadul Islam, MD Abdullah Al Nasim + 3 more
Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering…
Lynn K. A. Sörensen, Sander M. Bohté, Dorina de Jong, Heleen A. Slagter + 1 more
Humans can rapidly recognize objects in a dynamically changing world. This ability is showcased by the fact that observers succeed at recognizing objects in rapidly changing image sequences, at up to 13 ms/image. To date, the mechanisms that govern dynamic object recognition remain poorly understood. Here, we developed…
Simon Davidson, Steve B. Furber
Despite the success of Deep Neural Networks-a type of Artificial Neural Network (ANN)-in problem domains such as image recognition and speech processing, the energy and processing demands during both training and deployment are growing at an unsustainable rate in the push for greater accuracy. There is a temptation to…
Authors not listed
The automatic generation of image captions in natural language is a critical and challenging task, particularly in the context of environmental monitoring and control. This paper presents a novel deep learning-driven image captioning system designed for real-time monitoring and predictive control of pollutant gas…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Authors not listed
The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…